A portal dosimetry dose prediction method based on collapsed cone algorithm using the clinical beam model

Ortega, J. Martínez; González, Nuria; Castro, Pablo; Monedero, M. Pinto; Tolani, Naresh; Martín, Luis Núñez; Montero, Rocío Sánchez

doi:10.1002/mp.12018

Cited by 7 publications

(4 citation statements)

References 39 publications

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“…Gamma pass rates (3%, 3 mm DTA) of >94% were achieved for IMRT and VMAT fields applied to inhomogeneous and anthropomorphic phantoms. Martinez-Ortega et al [22] used Pinnacle (v8.0m) and introduced a water-equivalent virtual EPID model to the TPS but also implemented a photon spectral correction based on radiological thickness of the patient along individual raylines. They compared predictions to a-Si EPID measurements that were converted to be water-equivalent, and achieved gamma pass rates (5%, 3 mm DTA) of >80.3% for three square fields on an anthropomorphic phantom (cranial, thorax, and pelvis sites) and >94.6% for five IMRT fields on the pelvis site.…”

Section: Predictive Methods or Algorithmsmentioning

confidence: 99%

EPID-based in vivo dosimetry – new developments and applications

McCurdy

2023

J. Phys.: Conf. Ser.

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In vivo dosimetry has been shown to be a powerful quality assurance method in modern radiation therapy. The most common tool used for in vivo dosimetry is the electronic portal imaging device (EPID) which can quantitatively image the therapeutic beam fluence exiting the patient during treatment delivery. Since the last major literature review on this topic was published five years ago, the radiation oncology community has shown continued strong interest in this subject. Commercial options have become more widely available, with a related increase in validation efforts and sensitivity testing, while new applications continue to be explored. Work has been done to understand and increase the accuracy of the EPID for dosimetric applications, as well as continued efforts to provide practical, quantitative experiences from clinical implementation of in vivo dosimetry systems. This review examines the published literature related to in vivo EPID dosimetry from January 2017 to February 2022. The literature is classified into three main topical areas: (1) new or improved algorithmic developments including validation work, (2) applications of the in vivo EPID dosimetry method, and (3) error identification and error sensitivity analyses.

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Section: Predictive Methods or Algorithmsmentioning

confidence: 99%

EPID-based in vivo dosimetry – new developments and applications

McCurdy

2023

J. Phys.: Conf. Ser.

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“…Currently, amorphous silicon electronic portal imaging devices (a-Si EPIDs), which have high spatial resolutions, large twodimensional arrays, and approximately linear dose responses (11)(12)(13), are commonly used in clinical in vivo dosimetry (14)(15)(16)(17)(18). Although gamma pass rate threshold-based EPID in vivo dosimetry can be used to monitor treatment through single pass rate values (16)(17)(18)(19), research on EPID dosimetry by Hsieh Emmelyn S et al (20) has revealed that, under 3%/3 mm and 95% pass rate threshold criteria, position errors greater than 2 cm can be detected, which is unsatisfactory.…”

Section: Introductionmentioning

confidence: 99%

Analysis of EPID Transmission Fluence Maps Using Machine Learning Models and CNN for Identifying Position Errors in the Treatment of GO Patients

et al. 2021

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PurposeTo find a suitable method for analyzing electronic portal imaging device (EPID) transmission fluence maps for the identification of position errors in the in vivo dose monitoring of patients with Graves’ ophthalmopathy (GO).MethodsPosition errors combining 0-, 2-, and 4-mm errors in the left-right (LR), anterior-posterior (AP), and superior-inferior (SI) directions in the delivery of 40 GO patient radiotherapy plans to a human head phantom were simulated and EPID transmission fluence maps were acquired. Dose difference (DD) and structural similarity (SSIM) maps were calculated to quantify changes in the fluence maps. Three types of machine learning (ML) models that utilize radiomics features of the DD maps (ML 1 models), features of the SSIM maps (ML 2 models), and features of both DD and SSIM maps (ML 3 models) as inputs were used to perform three types of position error classification, namely a binary classification of the isocenter error (type 1), three binary classifications of LR, SI, and AP direction errors (type 2), and an eight-element classification of the combined LR, SI, and AP direction errors (type 3). Convolutional neural network (CNN) was also used to classify position errors using the DD and SSIM maps as input.ResultsThe best-performing ML 1 model was XGBoost, which achieved accuracies of 0.889, 0.755, 0.778, 0.833, and 0.532 in the type 1, type 2-LR, type 2-AP, type 2-SI, and type 3 classification, respectively. The best ML 2 model was XGBoost, which achieved accuracies of 0.856, 0.731, 0.736, 0.949, and 0.491, respectively. The best ML 3 model was linear discriminant classifier (LDC), which achieved accuracies of 0.903, 0.792, 0.870, 0.931, and 0.671, respectively. The CNN achieved classification accuracies of 0.925, 0.833, 0.875, 0.949, and 0.689, respectively.ConclusionML models and CNN using combined DD and SSIM maps can analyze EPID transmission fluence maps to identify position errors in the treatment of GO patients. Further studies with large sample sizes are needed to improve the accuracy of CNN.

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“…This method is widely used in clinics, but may not reflect the errors introduced in the TPS calculation process. Second, the EPID image is converted into a dose value and compared with an independent verification system [6,[11][12][13]. Many studies have shown that EPID has the potential to be used as a direct dosimetry tool [14][15][16][17][18] and to detect patient related errors during radiotherapy [19,20].…”

Section: Introductionmentioning

confidence: 99%

“…When some of Varian's EPID are exploited for dose measurement, two factors may affect the dosimetric properties of EPID. One factor is that EPID is mounted on the linac by means of a support arm, and the low-energy scatter ray produced by the interaction between X-rays and the support arm may lead to asymmetry of the EPID image in the inline direction [13,[21][22][23][24][25]; relevant studies show that the effect may exceed 5% [26,27]. The other factor is that EPID requires dark field and flood field calibration to ensure that the response of each pixel is constant, which results in the off-axis response of EPID being different from the actual output of the linac [28,29].…”

Section: Introductionmentioning

confidence: 99%

Development of an Electronic Portal Imaging Device Dosimetry Method

et al. 2021

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Support arm backscatter and off-axis effects of an electronic portal imaging device (EPID) are challenging for radiotherapy quality assurance. Aiming at the issue, we proposed a simple yet effective method with correction matrices to rectify backscatter and off-axis responses for EPID images. First, we measured the square fields with ionization chamber array (ICA) and EPID simultaneously. Second, we calculated the dose-to-pixel value ratio and used it as the correction matrix of the corresponding field. Third, the correction value of the large field was replaced with that of the same point in the small field to generate a correction matrix suitable for different EPID images. Finally, we rectified the EPID image with the correction matrix, and then the processed EPID images were converted into the absolute dose. The calculated dose was compared with the measured dose via ICA. The gamma pass rates of 3%/3 mm and 2%/2 mm (5% threshold) were 99.6% ± 0.94% and 95.48% ± 1.03%, and the average gamma values were 0.28 ± 0.04 and 0.42 ± 0.05, respectively. Experimental results verified our method accurately corrected EPID images and converted pixel values into absolute dose values such that EPID was an efficient radiotherapy dosimetry tool.

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A portal dosimetry dose prediction method based on collapsed cone algorithm using the clinical beam model

Cited by 7 publications

References 39 publications

EPID-based in vivo dosimetry – new developments and applications

EPID-based in vivo dosimetry – new developments and applications

Analysis of EPID Transmission Fluence Maps Using Machine Learning Models and CNN for Identifying Position Errors in the Treatment of GO Patients

Development of an Electronic Portal Imaging Device Dosimetry Method

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